Dengue Fever Outbreak Prediction in Surabaya using A Geographically Weighted Regression

Halim, Siana and Octavia, Tanti and Felecia, and Handojo, Andreas (2019) Dengue Fever Outbreak Prediction in Surabaya using A Geographically Weighted Regression. In: The 4th Technology Innovation Management and Engineering Science International Conference (TIMES-iCO, 14-12-2019 - 14-12-2019, Bangkok - Thailand.

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Abstract

Dengue Fever is one of the viral diseases of the tropics that are easily spread in high density and humid area such as in Surabaya. Many researchers in various expertise have studied this disease. Some of them use statistical and machine learning approach to predict the outbreak of the disease, so that the government can prevent that incident. In this paper we use the geographically weighted regression for predicting the dengue fever outbreak in Surabaya. The geographically weighted regression has superiority in estimating the coefficient of the explanatory variables locally. So that, we can put more attention into the region with has high estimates coefficient parameters. Here, we look at the locally estimates of the dengue fever infected in the year 2016, 2017, population density and poverty percentage for predicting the dengue fever outbreak in the year 2018. In this study, the pattern of the predicted model can follow the pattern of the true dataset.

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